EDLD 610 Winter 2020 Final Project

Plot 1

Data is ranked in order of median income with the highest incomes at the top of the data set

The purpose of these plots will be to display the distribution of median salaries by college major, by specifically focusing on both the top 20 median salaries and bottom 20 median salaries

## # A tibble: 6 x 21
##    rank major_code major total   men women major_category share_women
##   <int>      <int> <chr> <int> <int> <int> <chr>                <dbl>
## 1     1       2419 PETR…  2339  2057   282 Engineering          0.121
## 2     2       2416 MINI…   756   679    77 Engineering          0.102
## 3     3       2415 META…   856   725   131 Engineering          0.153
## 4     4       2417 NAVA…  1258  1123   135 Engineering          0.107
## 5     5       2405 CHEM… 32260 21239 11021 Engineering          0.342
## 6     6       2418 NUCL…  2573  2200   373 Engineering          0.145
## # … with 13 more variables: sample_size <int>, employed <int>,
## #   full_time <int>, part_time <int>, full_time_year_round <int>,
## #   unemployed <int>, unemployment_rate <dbl>, median <int>, p25th <int>,
## #   p75th <int>, college_jobs <int>, non_college_jobs <int>,
## #   low_wage_jobs <int>

## # A tibble: 6 x 9
##    rank major_code major major_category total   men women share_women
##   <int>      <int> <chr> <chr>          <int> <int> <int>       <dbl>
## 1     1       2419 PETR… Engineering     2339  2057   282       0.121
## 2     2       2416 MINI… Engineering      756   679    77       0.102
## 3     3       2415 META… Engineering      856   725   131       0.153
## 4     4       2417 NAVA… Engineering     1258  1123   135       0.107
## 5     5       2418 NUCL… Engineering     2573  2200   373       0.145
## 6     6       2405 CHEM… Engineering    32260 21239 11021       0.342
## # … with 1 more variable: median <int>

## # A tibble: 173 x 4
##    major                                     median p25th  p75th
##    <chr>                                      <int> <int>  <int>
##  1 PETROLEUM ENGINEERING                     110000 95000 125000
##  2 MINING AND MINERAL ENGINEERING             75000 55000  90000
##  3 METALLURGICAL ENGINEERING                  73000 50000 105000
##  4 NAVAL ARCHITECTURE AND MARINE ENGINEERING  70000 43000  80000
##  5 CHEMICAL ENGINEERING                       65000 50000  75000
##  6 NUCLEAR ENGINEERING                        65000 50000 102000
##  7 ACTUARIAL SCIENCE                          62000 53000  72000
##  8 ASTRONOMY AND ASTROPHYSICS                 62000 31500 109000
##  9 MECHANICAL ENGINEERING                     60000 48000  70000
## 10 ELECTRICAL ENGINEERING                     60000 45000  72000
## # … with 163 more rows

Plot 2

The purpose of Plot 2 will be to communicate visualizations of the recent-grads data to display trends in unemployment rate per college major and major category

## # A tibble: 1 x 1
##   `mean(median)`
##            <dbl>
## 1         40151.

## # A tibble: 1 x 1
##   `mean(unemployment_rate_percentage)`
##                                  <dbl>
## 1                               0.0682

Plot 3

The purpose of plot 3 will be to use the recent_grads and women_stem data sets to compare trends in the percentage of women working in these fields

## # A tibble: 6 x 21
##    rank major_code major total   men women major_category share_women
##   <int>      <int> <chr> <int> <int> <int> <chr>                <dbl>
## 1     1       2419 PETR…  2339  2057   282 Engineering          0.121
## 2     2       2416 MINI…   756   679    77 Engineering          0.102
## 3     3       2415 META…   856   725   131 Engineering          0.153
## 4     4       2417 NAVA…  1258  1123   135 Engineering          0.107
## 5     5       2405 CHEM… 32260 21239 11021 Engineering          0.342
## 6     6       2418 NUCL…  2573  2200   373 Engineering          0.145
## # … with 13 more variables: sample_size <int>, employed <int>,
## #   full_time <int>, part_time <int>, full_time_year_round <int>,
## #   unemployed <int>, unemployment_rate <dbl>, median <int>, p25th <int>,
## #   p75th <int>, college_jobs <int>, non_college_jobs <int>,
## #   low_wage_jobs <int>

## # A tibble: 6 x 9
##    rank major_code major major_category total   men women share_women
##   <int>      <int> <chr> <chr>          <int> <int> <int>       <dbl>
## 1     1       2419 PETR… Engineering     2339  2057   282       0.121
## 2     2       2416 MINI… Engineering      756   679    77       0.102
## 3     3       2415 META… Engineering      856   725   131       0.153
## 4     4       2417 NAVA… Engineering     1258  1123   135       0.107
## 5     5       2418 NUCL… Engineering     2573  2200   373       0.145
## 6     6       2405 CHEM… Engineering    32260 21239 11021       0.342
## # … with 1 more variable: median <int>

College Major	Median Salary	Percent Women Employed	Percent Men Employed
Engineering
BIOMEDICAL ENGINEERING	60000	0.43784687	0.56215313
ARCHITECTURE	40000	0.45146489	0.54853511
MINING AND MINERAL ENGINEERING	75000	0.10185185	0.89814815
MECHANICAL ENGINEERING RELATED TECHNOLOGIES	40000	0.07745303	0.92254697
Physical Sciences
NUCLEAR, INDUSTRIAL RADIOLOGY, AND BIOLOGICAL TECHNOLOGIES	46000	0.75047259	0.24952741
OCEANOGRAPHY	44700	0.68899917	0.31100083
PHYSICS	45000	0.28193641	0.71806359
ATMOSPHERIC SCIENCES AND METEOROLOGY	35000	0.32129607	0.67870393
Computers & Mathematics
MATHEMATICS	45000	0.44809868	0.55190132
STATISTICS AND DECISION SCIENCE	45000	0.52647576	0.47352424
MATHEMATICS AND COMPUTER SCIENCE	42000	0.17898194	0.82101806
COMPUTER ADMINISTRATION MANAGEMENT AND SECURITY	37500	0.18088272	0.81911728
Biology & Life Science
PHARMACOLOGY	45000	0.70771850	0.29228150
NEUROSCIENCE	35000	0.63814682	0.36185318
GENETICS	40000	0.51554333	0.48445667
BIOCHEMICAL SCIENCES	37400	0.51540645	0.48459355
Health
MEDICAL ASSISTING SERVICES	42000	0.92780725	0.07219275
COMMUNICATION DISORDERS SCIENCES AND SERVICES	28000	0.96799812	0.03200188
PHARMACY PHARMACEUTICAL SCIENCES AND ADMINISTRATION	40000	0.63071632	0.36928368
HEALTH AND MEDICAL PREPARATORY PROGRAMS	33500	0.56664050	0.43335950